The use of radiation therapy to treat cancer is well known. Typically, radiation therapy involves directing a beam of high energy proton, photon, or electron radiation (“therapeutic radiation”) into a target volume a (e.g., a tumor or lesion).
Before a patient is treated with radiation, a treatment plan specific to that patient developed. The plan defines various aspects of the therapy using simulations and optimizations based on past experiences. For example, for intensity modulated radiation therapy (IMRT), the plan can specify the appropriate beam type (e.g., flattening filter free type) and the appropriate beam energy. Other parts of the plan can specify, for example, the angle of the beam relative to the patient, the beam shape, the placement of boluses and shields, and the like. In general, the purpose of the treatment plan is to deliver sufficient radiation to the target volume while minimizing exposure of surrounding healthy tissue to the radiation.
In IMRT, the planner's goal is to find a solution that is optimal with respect to multiple clinical goals that may be contradictory in the sense that an improvement toward one goal may have a detrimental effect on reaching another goal. For example, a treatment plan that spares the liver from receiving a dose of radiation may result in the stomach receiving too much radiation. These types of tradeoffs lead to an iterative process in which the planner creates different plans to find the one plan that is best suited to achieving the desired outcome.
For example, the planner defines a set of quality metrics, such as target homogeneity, critical organ sparing, and the like, and respective target values Qi for the metrics. For planning, the metrics are defined such that a smaller value is preferred over a larger value. The planner also defines a relative priority or weight wi for each of the quality metrics. The task of developing an optimal plan is then formulated as a quadratic cost function C: C=sum(wi(Qi(Qi-qi)2), where qi is the value of the quality metric that can be achieved for a particular treatment plan. The optimal plan is determined by minimizing the cost function C.
Often it is not easy to determine, an optimal plan based solely on the cost function. For instance, the optimal solution of the cost function may not necessarily describe the clinically best balance between quality metrics, or the dose distribution might have some undesirable features that are difficult to represent as a quality metric.
One way to assist the planner is a knowledge-based approach that automatically generates objective functions so that the resulting plan incorporates and reflects present practices utilized in creating the knowledge base. This typically captures the best practices utilized at a treatment center, but can also be based on larger knowledge bases of well-defined treatments gathered from multiple treatment centers.
Another way to assist the planner is to use a multi-criteria optimization (MCO) approach for treatment planning. Pareto surface navigation is an MCO technique that facilitates exploration of the tradeoffs between clinical goals. For a given et of clinical goals, a treatment plan is considered to be Pareto optimal if it satisfies the goals and none of the metrics can be improved without worsening at least one of the other metrics. The set of Pareto optimal plans define a Pareto surface related to the set of clinical goals. Movement along the Pareto surface results in tradeoffs between the clinical goals; some metrics will improve at the cost of worsening one or more other metrics. The planner can navigate along the Pareto surface and choose a final (optimized) radiation treatment plan that seems to be the best according to the criteria applied b the planner, or a treatment plan can be selected automatically based on its proximity to the Pareto surface.
Navigation n the vicinity of the Pareto surface requires that a well-selected sample set of plans has been generated to define the Pareto surface. To maintain fluent interactivity during the navigation, the plans are calculated beforehand, and thus an educated guess must first be made about the region where the planner wants to navigate, so that the Pareto surface spans that region. If, during navigation, the planner wants to move beyond the region spanned by the original sample set of plans, then it is possible to enlarge the sample set by generating and adding more plans. In such cases, it is desirable that the additional plans be generated quickly.
A methodology that efficiently and quickly generates additional plans would be valuable.